TY  - JOUR
A1  - Pabon, Nicolas A.
A1  - Xia, Yan
A1  - Estabrooks, Samuel K.
A1  - Ye, Zhaofeng
A1  - Herbrand, Amanda K.
A1  - Süß, Evelyn
A1  - Biondi, Ricardo M.
A1  - Assimon, Victoria A.
A1  - Gestwicki, Jason E.
A1  - Brodsky, Jeffrey L.
A1  - Camacho, Carlos J.
A1  - Bar-Joseph, Ziv
T1  - Predicting protein targets for drug-like compounds using transcriptomics
T2  - PLoS Computational Biology
N2  - An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chemical space. To change this paradigm, we validated a new pipeline that identifies small molecule-protein interactions and works even for compounds lacking similarity to known drugs. Based on differential mRNA profiles in multiple cell types exposed to drugs and in which gene knockdowns (KD) were conducted, we showed that drugs induce gene regulatory networks that correlate with those produced after silencing protein-coding genes. Next, we applied supervised machine learning to exploit drug-KD signature correlations and enriched our predictions using an orthogonal structure-based screen. As a proof-of-principle for this regimen, top-10/top-100 target prediction accuracies of 26% and 41%, respectively, were achieved on a validation set 152 FDA-approved drugs and 3104 potential targets. We then predicted targets for 1680 compounds and validated chemical interactors with four targets that have proven difficult to chemically modulate, including non-covalent inhibitors of HRAS and KRAS. Importantly, drug-target interactions manifest as gene expression correlations between drug treatment and both target gene KD and KD of genes that act up- or down-stream of the target, even for relatively weak binders. These correlations provide new insights on the cellular response of disrupting protein interactions and highlight the complex genetic phenotypes of drug treatment. With further refinement, our pipeline may accelerate the identification and development of novel chemical classes by screening compound-target interactions.
KW  - Gene expression
KW  - Crystal structure
KW  - Small molecules
KW  - Structural genomics
KW  - Ubiquitination
KW  - Drug therapy
KW  - Drug discovery
KW  - Drug interactions
Y1  - 2018
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/48459
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-484597
SN  - 1553-7358
SN  - 1553-734X
N1  - Copyright: © 2018 Pabon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
VL  - 14
IS  - (12): e1006651
SP  - 1
EP  - 24
PB  - Public Library of Science
CY  - San Francisco, Calif.
ER  -